Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations750000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.4 MiB
Average record size in memory118.0 B

Variable types

Numeric8
Categorical1

Alerts

Body_Temp is highly overall correlated with Calories and 2 other fieldsHigh correlation
Calories is highly overall correlated with Body_Temp and 2 other fieldsHigh correlation
Duration is highly overall correlated with Body_Temp and 2 other fieldsHigh correlation
Heart_Rate is highly overall correlated with Body_Temp and 2 other fieldsHigh correlation
Height is highly overall correlated with Sex and 1 other fieldsHigh correlation
Sex is highly overall correlated with Height and 1 other fieldsHigh correlation
Weight is highly overall correlated with Height and 1 other fieldsHigh correlation
id is uniformly distributed Uniform
id has unique values Unique

Reproduction

Analysis started2025-08-14 09:56:06.582586
Analysis finished2025-08-14 09:56:30.583587
Duration24 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id
Real number (ℝ)

Uniform  Unique 

Distinct750000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean374999.5
Minimum0
Maximum749999
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:30.979240image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37499.95
Q1187499.75
median374999.5
Q3562499.25
95-th percentile712499.05
Maximum749999
Range749999
Interquartile range (IQR)374999.5

Descriptive statistics

Standard deviation216506.5
Coefficient of variation (CV)0.57735142
Kurtosis-1.2
Mean374999.5
Median Absolute Deviation (MAD)187500
Skewness-1.9631002 × 10-15
Sum2.8124962 × 1011
Variance4.6875062 × 1010
MonotonicityStrictly increasing
2025-08-14T12:56:31.109374image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
499993 1
 
< 0.1%
499995 1
 
< 0.1%
499996 1
 
< 0.1%
499997 1
 
< 0.1%
499998 1
 
< 0.1%
499999 1
 
< 0.1%
500000 1
 
< 0.1%
500001 1
 
< 0.1%
500002 1
 
< 0.1%
Other values (749990) 749990
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
749999 1
< 0.1%
749998 1
< 0.1%
749997 1
< 0.1%
749996 1
< 0.1%
749995 1
< 0.1%
749994 1
< 0.1%
749993 1
< 0.1%
749992 1
< 0.1%
749991 1
< 0.1%
749990 1
< 0.1%

Sex
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.6 MiB
female
375721 
male
374279 

Length

Max length6
Median length6
Mean length5.0019227
Min length4

Characters and Unicode

Total characters3751442
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowfemale
3rd rowfemale
4th rowmale
5th rowfemale

Common Values

ValueCountFrequency (%)
female 375721
50.1%
male 374279
49.9%

Length

2025-08-14T12:56:31.218412image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-14T12:56:31.290934image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
ValueCountFrequency (%)
female 375721
50.1%
male 374279
49.9%

Most occurring characters

ValueCountFrequency (%)
e 1125721
30.0%
m 750000
20.0%
a 750000
20.0%
l 750000
20.0%
f 375721
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3751442
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1125721
30.0%
m 750000
20.0%
a 750000
20.0%
l 750000
20.0%
f 375721
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3751442
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1125721
30.0%
m 750000
20.0%
a 750000
20.0%
l 750000
20.0%
f 375721
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3751442
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1125721
30.0%
m 750000
20.0%
a 750000
20.0%
l 750000
20.0%
f 375721
 
10.0%

Age
Real number (ℝ)

Distinct60
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.420404
Minimum20
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:31.360589image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21
Q128
median40
Q352
95-th percentile70
Maximum79
Range59
Interquartile range (IQR)24

Descriptive statistics

Standard deviation15.175049
Coefficient of variation (CV)0.36636651
Kurtosis-0.74633817
Mean41.420404
Median Absolute Deviation (MAD)12
Skewness0.43639748
Sum31065303
Variance230.28211
MonotonicityNot monotonic
2025-08-14T12:56:31.459275image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 36390
 
4.9%
32 26858
 
3.6%
20 26196
 
3.5%
40 22442
 
3.0%
45 21612
 
2.9%
22 21225
 
2.8%
26 20783
 
2.8%
54 20598
 
2.7%
25 20592
 
2.7%
42 20206
 
2.7%
Other values (50) 513098
68.4%
ValueCountFrequency (%)
20 26196
3.5%
21 36390
4.9%
22 21225
2.8%
23 14619
1.9%
24 17817
2.4%
25 20592
2.7%
26 20783
2.8%
27 14458
 
1.9%
28 18028
2.4%
29 16774
2.2%
ValueCountFrequency (%)
79 4278
0.6%
78 1609
 
0.2%
77 1707
 
0.2%
76 1079
 
0.1%
75 4051
0.5%
74 3795
0.5%
73 5454
0.7%
72 5009
0.7%
71 6744
0.9%
70 3968
0.5%

Height
Real number (ℝ)

High correlation 

Distinct86
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.69769
Minimum126
Maximum222
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:31.555132image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum126
5-th percentile154
Q1164
median174
Q3185
95-th percentile195
Maximum222
Range96
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.824496
Coefficient of variation (CV)0.073409648
Kurtosis-0.83980016
Mean174.69769
Median Absolute Deviation (MAD)10
Skewness0.051777124
Sum1.3102326 × 108
Variance164.46769
MonotonicityNot monotonic
2025-08-14T12:56:31.647923image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161 26540
 
3.5%
187 26443
 
3.5%
169 24823
 
3.3%
179 23617
 
3.1%
174 22300
 
3.0%
182 21474
 
2.9%
181 20405
 
2.7%
184 20304
 
2.7%
164 20179
 
2.7%
171 19022
 
2.5%
Other values (76) 524893
70.0%
ValueCountFrequency (%)
126 1
 
< 0.1%
129 1
 
< 0.1%
135 2
 
< 0.1%
136 2
 
< 0.1%
137 2
 
< 0.1%
138 4
 
< 0.1%
139 5
 
< 0.1%
140 19
 
< 0.1%
141 58
< 0.1%
142 50
< 0.1%
ValueCountFrequency (%)
222 3
 
< 0.1%
218 3
 
< 0.1%
217 6
 
< 0.1%
214 20
 
< 0.1%
213 46
 
< 0.1%
212 98
 
< 0.1%
211 154
< 0.1%
210 122
< 0.1%
209 145
< 0.1%
208 295
< 0.1%

Weight
Real number (ℝ)

High correlation 

Distinct91
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.145668
Minimum36
Maximum132
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:31.751205image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile55
Q163
median74
Q387
95-th percentile99
Maximum132
Range96
Interquartile range (IQR)24

Descriptive statistics

Standard deviation13.982704
Coefficient of variation (CV)0.18607465
Kurtosis-0.99607749
Mean75.145668
Median Absolute Deviation (MAD)12
Skewness0.21119386
Sum56359251
Variance195.516
MonotonicityNot monotonic
2025-08-14T12:56:31.849389image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61 27969
 
3.7%
64 26860
 
3.6%
63 21464
 
2.9%
60 20656
 
2.8%
62 20303
 
2.7%
81 19995
 
2.7%
88 19656
 
2.6%
91 18979
 
2.5%
59 18700
 
2.5%
68 18205
 
2.4%
Other values (81) 537213
71.6%
ValueCountFrequency (%)
36 1
 
< 0.1%
37 1
 
< 0.1%
39 1
 
< 0.1%
40 2
 
< 0.1%
41 3
 
< 0.1%
42 3
 
< 0.1%
43 4
 
< 0.1%
44 36
< 0.1%
45 74
< 0.1%
46 57
< 0.1%
ValueCountFrequency (%)
132 2
 
< 0.1%
128 3
 
< 0.1%
126 3
 
< 0.1%
124 1
 
< 0.1%
123 10
 
< 0.1%
122 3
 
< 0.1%
121 46
< 0.1%
120 1
 
< 0.1%
119 17
 
< 0.1%
118 18
 
< 0.1%

Duration
Real number (ℝ)

High correlation 

Distinct30
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.421015
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:31.929743image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q18
median15
Q323
95-th percentile28
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.3540954
Coefficient of variation (CV)0.54173448
Kurtosis-1.1953904
Mean15.421015
Median Absolute Deviation (MAD)7
Skewness0.026258951
Sum11565761
Variance69.79091
MonotonicityNot monotonic
2025-08-14T12:56:32.020725image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
9 28900
 
3.9%
26 28768
 
3.8%
19 28433
 
3.8%
15 28084
 
3.7%
11 27967
 
3.7%
25 27765
 
3.7%
5 27493
 
3.7%
28 27239
 
3.6%
16 27215
 
3.6%
8 26743
 
3.6%
Other values (20) 471393
62.9%
ValueCountFrequency (%)
1 12648
1.7%
2 23689
3.2%
3 26153
3.5%
4 25969
3.5%
5 27493
3.7%
6 26102
3.5%
7 25061
3.3%
8 26743
3.6%
9 28900
3.9%
10 26723
3.6%
ValueCountFrequency (%)
30 12464
1.7%
29 23856
3.2%
28 27239
3.6%
27 24758
3.3%
26 28768
3.8%
25 27765
3.7%
24 24749
3.3%
23 22360
3.0%
22 23208
3.1%
21 26109
3.5%

Heart_Rate
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.483995
Minimum67
Maximum128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:32.110586image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile80
Q188
median95
Q3103
95-th percentile111
Maximum128
Range61
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.4498454
Coefficient of variation (CV)0.098967847
Kurtosis-0.67656296
Mean95.483995
Median Absolute Deviation (MAD)7
Skewness-0.0056681583
Sum71612996
Variance89.299577
MonotonicityNot monotonic
2025-08-14T12:56:32.199115image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
91 34383
 
4.6%
99 29392
 
3.9%
102 28987
 
3.9%
94 27837
 
3.7%
101 27324
 
3.6%
96 27002
 
3.6%
90 26765
 
3.6%
89 25360
 
3.4%
93 25144
 
3.4%
104 25093
 
3.3%
Other values (53) 472713
63.0%
ValueCountFrequency (%)
67 80
 
< 0.1%
68 53
 
< 0.1%
69 255
 
< 0.1%
70 173
 
< 0.1%
71 305
 
< 0.1%
72 658
 
0.1%
73 1045
 
0.1%
74 2033
0.3%
75 2156
0.3%
76 4064
0.5%
ValueCountFrequency (%)
128 28
 
< 0.1%
127 4
 
< 0.1%
126 4
 
< 0.1%
125 56
 
< 0.1%
124 3
 
< 0.1%
123 23
 
< 0.1%
122 78
 
< 0.1%
121 185
< 0.1%
120 207
< 0.1%
119 400
0.1%

Body_Temp
Real number (ℝ)

High correlation 

Distinct75
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.036253
Minimum37.1
Maximum41.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:32.300932image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum37.1
5-th percentile38.5
Q139.6
median40.3
Q340.7
95-th percentile40.9
Maximum41.5
Range4.4
Interquartile range (IQR)1.1

Descriptive statistics

Standard deviation0.77987456
Coefficient of variation (CV)0.019479209
Kurtosis0.51962416
Mean40.036253
Median Absolute Deviation (MAD)0.4
Skewness-1.0223613
Sum30027190
Variance0.60820433
MonotonicityNot monotonic
2025-08-14T12:56:32.414182image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.7 67250
 
9.0%
40.8 52686
 
7.0%
40.5 49531
 
6.6%
40.3 49086
 
6.5%
40.6 47992
 
6.4%
40.4 41996
 
5.6%
40.9 39896
 
5.3%
40.1 36019
 
4.8%
40.2 32113
 
4.3%
39.7 30919
 
4.1%
Other values (65) 302512
40.3%
ValueCountFrequency (%)
37.1 22
 
< 0.1%
37.2 75
 
< 0.1%
37.3 189
 
< 0.1%
37.4 947
 
0.1%
37.5 975
 
0.1%
37.6 1160
 
0.2%
37.7 4412
0.6%
37.8 3362
0.4%
37.9 3777
0.5%
38 3870
0.5%
ValueCountFrequency (%)
41.5 59
 
< 0.1%
41.4 165
 
< 0.1%
41.3 1426
 
0.2%
41.2 3424
 
0.5%
41.1 7999
 
1.1%
41.03 1
 
< 0.1%
41 15443
 
2.1%
40.9 39896
5.3%
40.897 1
 
< 0.1%
40.8 52686
7.0%

Calories
Real number (ℝ)

High correlation 

Distinct277
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.282781
Minimum1
Maximum314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.7 MiB
2025-08-14T12:56:32.533947image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q134
median77
Q3136
95-th percentile200
Maximum314
Range313
Interquartile range (IQR)102

Descriptive statistics

Standard deviation62.395349
Coefficient of variation (CV)0.70676692
Kurtosis-0.68951314
Mean88.282781
Median Absolute Deviation (MAD)48
Skewness0.53919626
Sum66212086
Variance3893.1796
MonotonicityNot monotonic
2025-08-14T12:56:32.643144image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7 8350
 
1.1%
13 7261
 
1.0%
11 6982
 
0.9%
12 6951
 
0.9%
17 6946
 
0.9%
8 6916
 
0.9%
20 6895
 
0.9%
9 6650
 
0.9%
4 6353
 
0.8%
10 6185
 
0.8%
Other values (267) 680511
90.7%
ValueCountFrequency (%)
1 650
 
0.1%
2 2431
 
0.3%
3 4750
0.6%
4 6353
0.8%
5 4242
0.6%
6 5304
0.7%
7 8350
1.1%
8 6916
0.9%
9 6650
0.9%
10 6185
0.8%
ValueCountFrequency (%)
314 26
 
< 0.1%
300 32
 
< 0.1%
295 81
< 0.1%
289 32
 
< 0.1%
287 45
< 0.1%
280 41
< 0.1%
276 55
< 0.1%
273 29
 
< 0.1%
272 42
< 0.1%
271 85
< 0.1%

Interactions

2025-08-14T12:56:27.250655image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:15.634111image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:17.382761image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:18.986022image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:20.499525image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:22.010721image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:23.651685image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:25.536892image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:27.471945image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:15.991696image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:17.572259image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:19.184666image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:20.690853image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:22.207552image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:23.861998image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:25.740755image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:27.699263image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:16.191926image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:17.762578image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:19.371860image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:20.881409image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:22.407699image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:24.087091image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:25.934175image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:27.977659image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:16.389625image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:17.951568image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:19.556942image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:21.062395image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:22.605060image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:24.288716image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:26.132972image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:28.298776image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:16.584509image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:18.140592image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:19.741876image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:21.247974image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:22.811737image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:24.518509image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:26.336669image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:28.651894image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:16.783818image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:18.336007image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:19.929069image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:21.438271image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:23.021810image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:24.754973image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:26.597756image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:28.925137image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:16.981710image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:18.530818image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:20.123089image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:21.630552image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:23.235351image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:25.000810image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:26.825714image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:29.109324image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:17.174656image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:18.722353image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:20.308241image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:21.819571image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:23.453667image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:25.254233image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
2025-08-14T12:56:27.029589image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/

Correlations

2025-08-14T12:56:32.749255image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
AgeBody_TempCaloriesDurationHeart_RateHeightSexWeightid
Age1.0000.0250.1170.0110.0080.0160.0410.0760.002
Body_Temp0.0251.0000.9340.9450.842-0.0270.038-0.0180.001
Calories0.1170.9341.0000.9810.927-0.0290.145-0.0130.002
Duration0.0110.9450.9811.0000.885-0.0310.034-0.0220.002
Heart_Rate0.0080.8420.9270.8851.000-0.0170.035-0.0070.001
Height0.016-0.027-0.029-0.031-0.0171.0000.7750.962-0.000
Sex0.0410.0380.1450.0340.0350.7751.0000.8590.000
Weight0.076-0.018-0.013-0.022-0.0070.9620.8591.0000.001
id0.0020.0010.0020.0020.001-0.0000.0000.0011.000

Missing values

2025-08-14T12:56:29.238436image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-14T12:56:29.680629image/svg+xmlMatplotlib v3.10.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idSexAgeHeightWeightDurationHeart_RateBody_TempCalories
00male36189.082.026.0101.041.0150.0
11female64163.060.08.085.039.734.0
22female51161.064.07.084.039.829.0
33male20192.090.025.0105.040.7140.0
44female38166.061.025.0102.040.6146.0
55female26156.056.019.0100.040.5103.0
66female21172.073.03.081.038.39.0
77male46188.094.023.0100.040.8145.0
88female33166.063.025.0107.040.5161.0
99male65185.088.023.0104.041.0185.0
idSexAgeHeightWeightDurationHeart_RateBody_TempCalories
749990749990female58175.073.05.087.039.423.0
749991749991female42149.049.026.0108.040.7178.0
749992749992male29199.096.028.0108.041.0182.0
749993749993male25179.075.013.0101.040.166.0
749994749994female21157.058.011.086.039.542.0
749995749995male28193.097.030.0114.040.9230.0
749996749996female64165.063.018.092.040.596.0
749997749997male60162.067.029.0113.040.9221.0
749998749998male45182.091.017.0102.040.3109.0
749999749999female39171.065.019.097.040.6103.0